pi estimate via Monte Carlo simulation

pi Monte Carlo v2

#!/usr/bin/env python 
# Estimating the value of pi via Monte Carlo simulation

from random import random
import numpy as np
import matplotlib.pyplot as plt
 
trials = list(np.linspace(10,1000000, 1000))  # different number of trials
pi = []

def mc_multiple_runs(trials, hits = 0):
    '''
    (float, int) -> (float)
    This function returns the number of hits you get for each monte carlo run
    '''
    
    for i in range(int(trials)):
        x, y = random() , random()  # generate random x,y in (0,1] at each run 

        if x**2 + y**2 < 1 :  # defines the edge of the quadrant
            hits = hits + 1
    return float(hits)

for i in trials:
    pi.append(4*(mc_multiple_runs(i)/i)) 
    print 'hits : %d, trials: %d, estimate pi = %1.4F' %(mc_multiple_runs(i), i, 4*(mc_multiple_runs(i)/i) )

# plot graphs
plt.plot(trials, pi, 'g')
plt.title('Estimating the value of pi via Monte Carlo')
plt.xlabel('# of Trials')
plt.ylabel('Estimated value of pi')
plt.ylim(3.11,3.17)
plt.show()

plt.hist(pi, bins = np.linspace(3.12,3.16,50), color='green')
plt.title('Estimating the value of pi via Monte Carlo')
plt.xlabel('Estimated value of pi')
plt.ylabel('Trials')
plt.xlim(3.13,3.15)
plt.show()